Comparing Dataset A and Dataset B

Dataset statistics

	Dataset A	Dataset B
Number of variables	12	12
Number of observations	446	446
Missing cells	433	449
Missing cells (%)	8.1%	8.4%
Duplicate rows	0	0
Duplicate rows (%)	0.0%	0.0%
Total size in memory	45.3 KiB	45.3 KiB
Average record size in memory	104.0 B	104.0 B

Variable types

	Dataset A	Dataset B
Numeric	5	5
Categorical	4	4
Text	3	3

Alerts

Dataset A	Dataset B
`Sex` is highly overall correlated with `Survived`	`Sex` is highly overall correlated with `Survived`	High correlation
`Survived` is highly overall correlated with `Sex`	`Survived` is highly overall correlated with `Sex`	High correlation
`Age` has 82 (18.4%) missing values	`Age` has 94 (21.1%) missing values	Missing
`Cabin` has 350 (78.5%) missing values	`Cabin` has 354 (79.4%) missing values	Missing
`PassengerId` has unique values	`PassengerId` has unique values	Unique
`Name` has unique values	`Name` has unique values	Unique
`SibSp` has 300 (67.3%) zeros	`SibSp` has 302 (67.7%) zeros	Zeros
`Parch` has 341 (76.5%) zeros	`Parch` has 334 (74.9%) zeros	Zeros
`Fare` has 10 (2.2%) zeros	`Fare` has 9 (2.0%) zeros	Zeros

Reproduction

	Dataset A	Dataset B
Analysis started	2025-03-18 21:18:17.884531	2025-03-18 21:18:19.968246
Analysis finished	2025-03-18 21:18:19.965425	2025-03-18 21:18:21.993726
Duration	2.08 seconds	2.03 seconds
Software version	ydata-profiling v0.0.dev0	ydata-profiling v0.0.dev0
Download configuration	config.json	config.json

PassengerId
Real number (ℝ)

	Dataset A	Dataset B
Distinct	446	446
Distinct (%)	100.0%	100.0%
Missing	0	0
Missing (%)	0.0%	0.0%
Infinite	0	0
Infinite (%)	0.0%	0.0%
Mean	453.23318	443.73094

	Dataset A	Dataset B
Minimum	1	1
Maximum	890	891
Zeros	0	0
Zeros (%)	0.0%	0.0%
Negative	0	0
Negative (%)	0.0%	0.0%
Memory size	7.0 KiB	7.0 KiB

Quantile statistics

	Dataset A	Dataset B
Minimum	1	1
5-th percentile	45.75	36.25
Q1	240.5	214.5
median	447.5	453.5
Q3	674.5	663.5
95-th percentile	846.25	843.25
Maximum	890	891
Range	889	890
Interquartile range (IQR)	434	449

Descriptive statistics

	Dataset A	Dataset B
Standard deviation	256.61237	259.5166
Coefficient of variation (CV)	0.56618178	0.58485126
Kurtosis	-1.1829226	-1.196814
Mean	453.23318	443.73094
Median Absolute Deviation (MAD)	219	218.5
Skewness	-0.036868491	-0.037108928
Sum	202142	197904
Variance	65849.91	67348.867
Monotonicity	Not monotonic	Not monotonic

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
572	1	0.2%
624	1	0.2%
886	1	0.2%
379	1	0.2%
853	1	0.2%
127	1	0.2%
319	1	0.2%
733	1	0.2%
517	1	0.2%
235	1	0.2%
Other values (436)	436	97.8%

Value	Count	Frequency (%)
220	1	0.2%
173	1	0.2%
27	1	0.2%
756	1	0.2%
707	1	0.2%
592	1	0.2%
798	1	0.2%
148	1	0.2%
424	1	0.2%
518	1	0.2%
Other values (436)	436	97.8%

Value	Count	Frequency (%)
1	1	0.2%
4	1	0.2%
5	1	0.2%
6	1	0.2%
9	1	0.2%
11	1	0.2%
12	1	0.2%
17	1	0.2%
19	1	0.2%
21	1	0.2%

Value	Count	Frequency (%)
1	1	0.2%
6	1	0.2%
7	1	0.2%
8	1	0.2%
11	1	0.2%
12	1	0.2%
14	1	0.2%
16	1	0.2%
18	1	0.2%
19	1	0.2%

Value	Count	Frequency (%)
1	1	0.2%
6	1	0.2%
7	1	0.2%
8	1	0.2%
11	1	0.2%
12	1	0.2%
14	1	0.2%
16	1	0.2%
18	1	0.2%
19	1	0.2%

Value	Count	Frequency (%)
1	1	0.2%
4	1	0.2%
5	1	0.2%
6	1	0.2%
9	1	0.2%
11	1	0.2%
12	1	0.2%
17	1	0.2%
19	1	0.2%
21	1	0.2%

Survived
Categorical

	Dataset A	Dataset B
Distinct	2	2
Distinct (%)	0.4%	0.4%
Missing	0	0
Missing (%)	0.0%	0.0%
Memory size	7.0 KiB	7.0 KiB

0	274
1	172

0	275
1	171

Length

	Dataset A	Dataset B
Max length	1	1
Median length	1	1
Mean length	1	1
Min length	1	1

Characters and Unicode

	Dataset A	Dataset B
Total characters	446	446
Distinct characters	2	2
Distinct categories	1	1 ?
Distinct scripts	1	1 ?
Distinct blocks	1	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

	Dataset A	Dataset B
Unique	0	0 ?
Unique (%)	0.0%	0.0%

Sample

	Dataset A	Dataset B
1st row	0	1
2nd row	0	0
3rd row	0	1
4th row	0	1
5th row	0	1

Common Values

Value	Count	Frequency (%)
0	274	61.4%
1	172	38.6%

Value	Count	Frequency (%)
0	275	61.7%
1	171	38.3%

Length

Histogram of lengths of the category

Common Values (Plot)

Dataset A

Dataset B

Value	Count	Frequency (%)
0	274	61.4%
1	172	38.6%

Value	Count	Frequency (%)
0	275	61.7%
1	171	38.3%

Most occurring characters

Value	Count	Frequency (%)
0	274	61.4%
1	172	38.6%

Value	Count	Frequency (%)
0	275	61.7%
1	171	38.3%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	446	100.0%

Value	Count	Frequency (%)
(unknown)	446	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
0	274	61.4%
1	172	38.6%

Value	Count	Frequency (%)
0	275	61.7%
1	171	38.3%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	446	100.0%

Value	Count	Frequency (%)
(unknown)	446	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
0	274	61.4%
1	172	38.6%

Value	Count	Frequency (%)
0	275	61.7%
1	171	38.3%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	446	100.0%

Value	Count	Frequency (%)
(unknown)	446	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
0	274	61.4%
1	172	38.6%

Value	Count	Frequency (%)
0	275	61.7%
1	171	38.3%

Pclass
Categorical

	Dataset A	Dataset B
Distinct	3	3
Distinct (%)	0.7%	0.7%
Missing	0	0
Missing (%)	0.0%	0.0%
Memory size	7.0 KiB	7.0 KiB

3	244
1	108
2	94

3	253
1	98
2	95

Length

	Dataset A	Dataset B
Max length	1	1
Median length	1	1
Mean length	1	1
Min length	1	1

Characters and Unicode

	Dataset A	Dataset B
Total characters	446	446
Distinct characters	3	3
Distinct categories	1	1 ?
Distinct scripts	1	1 ?
Distinct blocks	1	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

	Dataset A	Dataset B
Unique	0	0 ?
Unique (%)	0.0%	0.0%

Sample

	Dataset A	Dataset B
1st row	3	3
2nd row	3	3
3rd row	3	2
4th row	3	2
5th row	3	1

Common Values

Value	Count	Frequency (%)
3	244	54.7%
1	108	24.2%
2	94	21.1%

Value	Count	Frequency (%)
3	253	56.7%
1	98	22.0%
2	95	21.3%

Length

Histogram of lengths of the category

Common Values (Plot)

Dataset A

Dataset B

Value	Count	Frequency (%)
3	244	54.7%
1	108	24.2%
2	94	21.1%

Value	Count	Frequency (%)
3	253	56.7%
1	98	22.0%
2	95	21.3%

Most occurring characters

Value	Count	Frequency (%)
3	244	54.7%
1	108	24.2%
2	94	21.1%

Value	Count	Frequency (%)
3	253	56.7%
1	98	22.0%
2	95	21.3%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	446	100.0%

Value	Count	Frequency (%)
(unknown)	446	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
3	244	54.7%
1	108	24.2%
2	94	21.1%

Value	Count	Frequency (%)
3	253	56.7%
1	98	22.0%
2	95	21.3%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	446	100.0%

Value	Count	Frequency (%)
(unknown)	446	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
3	244	54.7%
1	108	24.2%
2	94	21.1%

Value	Count	Frequency (%)
3	253	56.7%
1	98	22.0%
2	95	21.3%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	446	100.0%

Value	Count	Frequency (%)
(unknown)	446	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
3	244	54.7%
1	108	24.2%
2	94	21.1%

Value	Count	Frequency (%)
3	253	56.7%
1	98	22.0%
2	95	21.3%

Name
['Text', 'Text']

	Dataset A	Dataset B
Distinct	446	446
Distinct (%)	100.0%	100.0%
Missing	0	0
Missing (%)	0.0%	0.0%
Memory size	7.0 KiB	7.0 KiB

Length

	Dataset A	Dataset B
Max length	82	67
Median length	48	49
Mean length	27.345291	26.847534
Min length	12	12

Characters and Unicode

	Dataset A	Dataset B
Total characters	12196	11974
Distinct characters	60	60
Distinct categories	1	1 ?
Distinct scripts	1	1 ?
Distinct blocks	1	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

	Dataset A	Dataset B
Unique	446	446 ?
Unique (%)	100.0%	100.0%

Sample

	Dataset A	Dataset B
1st row	Hansen, Mr. Henry Damsgaard	Johnson, Miss. Eleanor Ileen
2nd row	Rice, Mrs. William (Margaret Norton)	Emir, Mr. Farred Chehab
3rd row	Betros, Mr. Tannous	Hamalainen, Master. Viljo
4th row	Boulos, Miss. Nourelain	Kelly, Mrs. Florence "Fannie"
5th row	McMahon, Mr. Martin	Stephenson, Mrs. Walter Bertram (Martha Eustis)

Value	Count	Frequency (%)
mr	251	13.6%
miss	91	4.9%
mrs	73	4.0%
william	37	2.0%
john	23	1.2%
henry	21	1.1%
master	19	1.0%
mary	13	0.7%
george	12	0.6%
james	11	0.6%
Other values (899)	1296	70.2%

Value	Count	Frequency (%)
mr	260	14.4%
miss	94	5.2%
mrs	67	3.7%
william	23	1.3%
john	23	1.3%
master	18	1.0%
henry	17	0.9%
george	14	0.8%
charles	12	0.7%
james	12	0.7%
Other values (876)	1267	70.1%

Most occurring characters

Value	Count	Frequency (%)
	1401	11.5%
r	992	8.1%
e	873	7.2%
a	834	6.8%
i	683	5.6%
n	642	5.3%
s	637	5.2%
l	583	4.8%
M	572	4.7%
o	532	4.4%
Other values (50)	4447	36.5%

Value	Count	Frequency (%)
	1363	11.4%
r	988	8.3%
e	856	7.1%
a	844	7.0%
n	642	5.4%
s	632	5.3%
i	629	5.3%
M	576	4.8%
l	524	4.4%
o	490	4.1%
Other values (50)	4430	37.0%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	12196	100.0%

Value	Count	Frequency (%)
(unknown)	11974	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
	1401	11.5%
r	992	8.1%
e	873	7.2%
a	834	6.8%
i	683	5.6%
n	642	5.3%
s	637	5.2%
l	583	4.8%
M	572	4.7%
o	532	4.4%
Other values (50)	4447	36.5%

Value	Count	Frequency (%)
	1363	11.4%
r	988	8.3%
e	856	7.1%
a	844	7.0%
n	642	5.4%
s	632	5.3%
i	629	5.3%
M	576	4.8%
l	524	4.4%
o	490	4.1%
Other values (50)	4430	37.0%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	12196	100.0%

Value	Count	Frequency (%)
(unknown)	11974	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
	1401	11.5%
r	992	8.1%
e	873	7.2%
a	834	6.8%
i	683	5.6%
n	642	5.3%
s	637	5.2%
l	583	4.8%
M	572	4.7%
o	532	4.4%
Other values (50)	4447	36.5%

Value	Count	Frequency (%)
	1363	11.4%
r	988	8.3%
e	856	7.1%
a	844	7.0%
n	642	5.4%
s	632	5.3%
i	629	5.3%
M	576	4.8%
l	524	4.4%
o	490	4.1%
Other values (50)	4430	37.0%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	12196	100.0%

Value	Count	Frequency (%)
(unknown)	11974	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
	1401	11.5%
r	992	8.1%
e	873	7.2%
a	834	6.8%
i	683	5.6%
n	642	5.3%
s	637	5.2%
l	583	4.8%
M	572	4.7%
o	532	4.4%
Other values (50)	4447	36.5%

Value	Count	Frequency (%)
	1363	11.4%
r	988	8.3%
e	856	7.1%
a	844	7.0%
n	642	5.4%
s	632	5.3%
i	629	5.3%
M	576	4.8%
l	524	4.4%
o	490	4.1%
Other values (50)	4430	37.0%

Sex
Categorical

	Dataset A	Dataset B
Distinct	2	2
Distinct (%)	0.4%	0.4%
Missing	0	0
Missing (%)	0.0%	0.0%
Memory size	7.0 KiB	7.0 KiB

male	280
female	166

male	284
female	162

Length

	Dataset A	Dataset B
Max length	6	6
Median length	4	4
Mean length	4.7443946	4.7264574
Min length	4	4

Characters and Unicode

	Dataset A	Dataset B
Total characters	2116	2108
Distinct characters	5	5
Distinct categories	1	1 ?
Distinct scripts	1	1 ?
Distinct blocks	1	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

	Dataset A	Dataset B
Unique	0	0 ?
Unique (%)	0.0%	0.0%

Sample

	Dataset A	Dataset B
1st row	male	female
2nd row	female	male
3rd row	male	male
4th row	female	female
5th row	male	female

Common Values

Value	Count	Frequency (%)
male	280	62.8%
female	166	37.2%

Value	Count	Frequency (%)
male	284	63.7%
female	162	36.3%

Length

Histogram of lengths of the category

Common Values (Plot)

Dataset A

Dataset B

Value	Count	Frequency (%)
male	280	62.8%
female	166	37.2%

Value	Count	Frequency (%)
male	284	63.7%
female	162	36.3%

Most occurring characters

Value	Count	Frequency (%)
e	612	28.9%
m	446	21.1%
a	446	21.1%
l	446	21.1%
f	166	7.8%

Value	Count	Frequency (%)
e	608	28.8%
m	446	21.2%
a	446	21.2%
l	446	21.2%
f	162	7.7%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	2116	100.0%

Value	Count	Frequency (%)
(unknown)	2108	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
e	612	28.9%
m	446	21.1%
a	446	21.1%
l	446	21.1%
f	166	7.8%

Value	Count	Frequency (%)
e	608	28.8%
m	446	21.2%
a	446	21.2%
l	446	21.2%
f	162	7.7%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	2116	100.0%

Value	Count	Frequency (%)
(unknown)	2108	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
e	612	28.9%
m	446	21.1%
a	446	21.1%
l	446	21.1%
f	166	7.8%

Value	Count	Frequency (%)
e	608	28.8%
m	446	21.2%
a	446	21.2%
l	446	21.2%
f	162	7.7%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	2116	100.0%

Value	Count	Frequency (%)
(unknown)	2108	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
e	612	28.9%
m	446	21.1%
a	446	21.1%
l	446	21.1%
f	166	7.8%

Value	Count	Frequency (%)
e	608	28.8%
m	446	21.2%
a	446	21.2%
l	446	21.2%
f	162	7.7%

Age
Real number (ℝ)

	Dataset A	Dataset B
Distinct	72	79
Distinct (%)	19.8%	22.4%
Missing	82	94
Missing (%)	18.4%	21.1%
Infinite	0	0
Infinite (%)	0.0%	0.0%
Mean	29.878434	29.587614

	Dataset A	Dataset B
Minimum	0.83	0.42
Maximum	74	74
Zeros	0	0
Zeros (%)	0.0%	0.0%
Negative	0	0
Negative (%)	0.0%	0.0%
Memory size	7.0 KiB	7.0 KiB

Quantile statistics

	Dataset A	Dataset B
Minimum	0.83	0.42
5-th percentile	4	4
Q1	20.875	21
median	28	28.75
Q3	38	38
95-th percentile	56	58
Maximum	74	74
Range	73.17	73.58
Interquartile range (IQR)	17.125	17

Descriptive statistics

	Dataset A	Dataset B
Standard deviation	14.433438	14.619999
Coefficient of variation (CV)	0.48307209	0.49412565
Kurtosis	0.15944868	0.19413525
Mean	29.878434	29.587614
Median Absolute Deviation (MAD)	8	8.25
Skewness	0.39265655	0.35147572
Sum	10875.75	10414.84
Variance	208.32412	213.74437
Monotonicity	Not monotonic	Not monotonic

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
24	18	4.0%
18	16	3.6%
28	15	3.4%
22	15	3.4%
35	15	3.4%
19	13	2.9%
36	12	2.7%
30	12	2.7%
23	11	2.5%
25	10	2.2%
Other values (62)	227	50.9%
(Missing)	82	18.4%

Value	Count	Frequency (%)
24	15	3.4%
22	15	3.4%
36	14	3.1%
30	12	2.7%
29	12	2.7%
28	12	2.7%
31	12	2.7%
18	12	2.7%
19	12	2.7%
33	11	2.5%
Other values (69)	225	50.4%
(Missing)	94	21.1%

Value	Count	Frequency (%)
0.83	1	0.2%
0.92	1	0.2%
1	2	0.4%
2	6	1.3%
3	6	1.3%
4	4	0.9%
6	2	0.4%
7	2	0.4%
8	1	0.2%
9	5	1.1%

Value	Count	Frequency (%)
0.42	1	0.2%
0.67	1	0.2%
0.75	2	0.4%
0.83	1	0.2%
0.92	1	0.2%
1	5	1.1%
2	3	0.7%
3	3	0.7%
4	5	1.1%
5	2	0.4%

Value	Count	Frequency (%)
0.42	1	0.2%
0.67	1	0.2%
0.75	2	0.4%
0.83	1	0.2%
0.92	1	0.2%
1	5	1.1%
2	3	0.7%
3	3	0.7%
4	5	1.1%
5	2	0.4%

Value	Count	Frequency (%)
0.83	1	0.2%
0.92	1	0.2%
1	2	0.4%
2	6	1.3%
3	6	1.3%
4	4	0.9%
6	2	0.4%
7	2	0.4%
8	1	0.2%
9	5	1.1%

SibSp
Real number (ℝ)

	Dataset A	Dataset B
Distinct	7	7
Distinct (%)	1.6%	1.6%
Missing	0	0
Missing (%)	0.0%	0.0%
Infinite	0	0
Infinite (%)	0.0%	0.0%
Mean	0.54035874	0.55829596

	Dataset A	Dataset B
Minimum	0	0
Maximum	8	8
Zeros	300	302
Zeros (%)	67.3%	67.7%
Negative	0	0
Negative (%)	0.0%	0.0%
Memory size	7.0 KiB	7.0 KiB

Quantile statistics

	Dataset A	Dataset B
Minimum	0	0
5-th percentile	0	0
Q1	0	0
median	0	0
Q3	1	1
95-th percentile	3	3
Maximum	8	8
Range	8	8
Interquartile range (IQR)	1	1

Descriptive statistics

	Dataset A	Dataset B
Standard deviation	1.0692574	1.1572772
Coefficient of variation (CV)	1.9787917	2.0728741
Kurtosis	13.655148	15.993364
Mean	0.54035874	0.55829596
Median Absolute Deviation (MAD)	0	0
Skewness	3.2015606	3.5058105
Sum	241	249
Variance	1.1433113	1.3392906
Monotonicity	Not monotonic	Not monotonic

Histogram with fixed size bins (bins=7)

Value	Count	Frequency (%)
0	300	67.3%
1	104	23.3%
2	16	3.6%
3	11	2.5%
4	9	2.0%
5	4	0.9%
8	2	0.4%

Value	Count	Frequency (%)
0	302	67.7%
1	101	22.6%
2	15	3.4%
3	12	2.7%
4	10	2.2%
8	4	0.9%
5	2	0.4%

Value	Count	Frequency (%)
0	300	67.3%
1	104	23.3%
2	16	3.6%
3	11	2.5%
4	9	2.0%
5	4	0.9%
8	2	0.4%

Value	Count	Frequency (%)
0	302	67.7%
1	101	22.6%
2	15	3.4%
3	12	2.7%
4	10	2.2%
5	2	0.4%
8	4	0.9%

Value	Count	Frequency (%)
0	302	67.7%
1	101	22.6%
2	15	3.4%
3	12	2.7%
4	10	2.2%
5	2	0.4%
8	4	0.9%

Value	Count	Frequency (%)
0	300	67.3%
1	104	23.3%
2	16	3.6%
3	11	2.5%
4	9	2.0%
5	4	0.9%
8	2	0.4%

Parch
Real number (ℝ)

	Dataset A	Dataset B
Distinct	7	6
Distinct (%)	1.6%	1.3%
Missing	0	0
Missing (%)	0.0%	0.0%
Infinite	0	0
Infinite (%)	0.0%	0.0%
Mean	0.39686099	0.4058296

	Dataset A	Dataset B
Minimum	0	0
Maximum	6	5
Zeros	341	334
Zeros (%)	76.5%	74.9%
Negative	0	0
Negative (%)	0.0%	0.0%
Memory size	7.0 KiB	7.0 KiB

Quantile statistics

	Dataset A	Dataset B
Minimum	0	0
5-th percentile	0	0
Q1	0	0
median	0	0
Q3	0	0.75
95-th percentile	2	2
Maximum	6	5
Range	6	5
Interquartile range (IQR)	0	0.75

Descriptive statistics

	Dataset A	Dataset B
Standard deviation	0.86213167	0.84739501
Coefficient of variation (CV)	2.172377	2.0880562
Kurtosis	10.403407	9.7373855
Mean	0.39686099	0.4058296
Median Absolute Deviation (MAD)	0	0
Skewness	2.8791806	2.7902118
Sum	177	181
Variance	0.74327102	0.7180783
Monotonicity	Not monotonic	Not monotonic

Histogram with fixed size bins (bins=7)

Value	Count	Frequency (%)
0	341	76.5%
1	55	12.3%
2	40	9.0%
5	3	0.7%
3	3	0.7%
4	3	0.7%
6	1	0.2%

Value	Count	Frequency (%)
0	334	74.9%
1	63	14.1%
2	41	9.2%
5	5	1.1%
4	2	0.4%
3	1	0.2%

Value	Count	Frequency (%)
0	341	76.5%
1	55	12.3%
2	40	9.0%
3	3	0.7%
4	3	0.7%
5	3	0.7%
6	1	0.2%

Value	Count	Frequency (%)
0	334	74.9%
1	63	14.1%
2	41	9.2%
3	1	0.2%
4	2	0.4%
5	5	1.1%

Value	Count	Frequency (%)
0	334	74.9%
1	63	14.1%
2	41	9.2%
3	1	0.2%
4	2	0.4%
5	5	1.1%

Value	Count	Frequency (%)
0	341	76.5%
1	55	12.3%
2	40	9.0%
3	3	0.7%
4	3	0.7%
5	3	0.7%
6	1	0.2%

Ticket
['Text', 'Text']

	Dataset A	Dataset B
Distinct	376	377
Distinct (%)	84.3%	84.5%
Missing	0	0
Missing (%)	0.0%	0.0%
Memory size	7.0 KiB	7.0 KiB

Length

	Dataset A	Dataset B
Max length	18	18
Median length	6	17
Mean length	6.661435	6.7488789
Min length	3	3

Characters and Unicode

	Dataset A	Dataset B
Total characters	2971	3010
Distinct characters	32	35
Distinct categories	1	1 ?
Distinct scripts	1	1 ?
Distinct blocks	1	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

	Dataset A	Dataset B
Unique	325	325 ?
Unique (%)	72.9%	72.9%

Sample

	Dataset A	Dataset B
1st row	350029	347742
2nd row	382652	2631
3rd row	2648	250649
4th row	2678	223596
5th row	370372	36947

Value	Count	Frequency (%)
pc	30	5.4%
c.a	12	2.2%
ca	8	1.4%
a/5	7	1.3%
2144	5	0.9%
c	5	0.9%
ston/o	5	0.9%
2	5	0.9%
1601	5	0.9%
sc/paris	5	0.9%
Other values (392)	468	84.3%

Value	Count	Frequency (%)
pc	27	4.8%
c.a	14	2.5%
a/5	8	1.4%
ca	7	1.2%
ston/o	7	1.2%
2	7	1.2%
a/4	6	1.1%
3101295	5	0.9%
sc/paris	5	0.9%
w./c	5	0.9%
Other values (398)	477	84.0%

Most occurring characters

Value	Count	Frequency (%)
3	374	12.6%
1	346	11.6%
2	291	9.8%
7	248	8.3%
4	238	8.0%
6	213	7.2%
0	209	7.0%
9	186	6.3%
5	180	6.1%
8	141	4.7%
Other values (22)	545	18.3%

Value	Count	Frequency (%)
3	360	12.0%
1	320	10.6%
2	285	9.5%
4	253	8.4%
7	245	8.1%
6	217	7.2%
0	205	6.8%
5	199	6.6%
9	181	6.0%
8	130	4.3%
Other values (25)	615	20.4%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	2971	100.0%

Value	Count	Frequency (%)
(unknown)	3010	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
3	374	12.6%
1	346	11.6%
2	291	9.8%
7	248	8.3%
4	238	8.0%
6	213	7.2%
0	209	7.0%
9	186	6.3%
5	180	6.1%
8	141	4.7%
Other values (22)	545	18.3%

Value	Count	Frequency (%)
3	360	12.0%
1	320	10.6%
2	285	9.5%
4	253	8.4%
7	245	8.1%
6	217	7.2%
0	205	6.8%
5	199	6.6%
9	181	6.0%
8	130	4.3%
Other values (25)	615	20.4%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	2971	100.0%

Value	Count	Frequency (%)
(unknown)	3010	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
3	374	12.6%
1	346	11.6%
2	291	9.8%
7	248	8.3%
4	238	8.0%
6	213	7.2%
0	209	7.0%
9	186	6.3%
5	180	6.1%
8	141	4.7%
Other values (22)	545	18.3%

Value	Count	Frequency (%)
3	360	12.0%
1	320	10.6%
2	285	9.5%
4	253	8.4%
7	245	8.1%
6	217	7.2%
0	205	6.8%
5	199	6.6%
9	181	6.0%
8	130	4.3%
Other values (25)	615	20.4%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	2971	100.0%

Value	Count	Frequency (%)
(unknown)	3010	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
3	374	12.6%
1	346	11.6%
2	291	9.8%
7	248	8.3%
4	238	8.0%
6	213	7.2%
0	209	7.0%
9	186	6.3%
5	180	6.1%
8	141	4.7%
Other values (22)	545	18.3%

Value	Count	Frequency (%)
3	360	12.0%
1	320	10.6%
2	285	9.5%
4	253	8.4%
7	245	8.1%
6	217	7.2%
0	205	6.8%
5	199	6.6%
9	181	6.0%
8	130	4.3%
Other values (25)	615	20.4%

Fare
Real number (ℝ)

	Dataset A	Dataset B
Distinct	169	180
Distinct (%)	37.9%	40.4%
Missing	0	0
Missing (%)	0.0%	0.0%
Infinite	0	0
Infinite (%)	0.0%	0.0%
Mean	31.779016	33.132539

	Dataset A	Dataset B
Minimum	0	0
Maximum	512.3292	512.3292
Zeros	10	9
Zeros (%)	2.2%	2.0%
Negative	0	0
Negative (%)	0.0%	0.0%
Memory size	7.0 KiB	7.0 KiB

Quantile statistics

	Dataset A	Dataset B
Minimum	0	0
5-th percentile	7.06875	7.225
Q1	7.8958	7.9031
median	14.4542	14.5
Q3	30.92395	29.925
95-th percentile	112.67708	118.31875
Maximum	512.3292	512.3292
Range	512.3292	512.3292
Interquartile range (IQR)	23.02815	22.0219

Descriptive statistics

	Dataset A	Dataset B
Standard deviation	50.310416	54.521731
Coefficient of variation (CV)	1.5831332	1.6455645
Kurtosis	40.129611	30.294074
Mean	31.779016	33.132539
Median Absolute Deviation (MAD)	7.0813	6.8125
Skewness	5.2875588	4.7158345
Sum	14173.441	14777.113
Variance	2531.1379	2972.6192
Monotonicity	Not monotonic	Not monotonic

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
13	24	5.4%
7.8958	19	4.3%
7.75	18	4.0%
8.05	16	3.6%
10.5	13	2.9%
26	13	2.9%
7.775	11	2.5%
7.925	10	2.2%
0	10	2.2%
26.55	10	2.2%
Other values (159)	302	67.7%

Value	Count	Frequency (%)
7.8958	21	4.7%
8.05	19	4.3%
13	17	3.8%
26	16	3.6%
7.75	15	3.4%
10.5	11	2.5%
0	9	2.0%
26.55	9	2.0%
7.925	9	2.0%
8.6625	8	1.8%
Other values (170)	312	70.0%

Value	Count	Frequency (%)
0	10	2.2%
4.0125	1	0.2%
5	1	0.2%
6.2375	1	0.2%
6.4375	1	0.2%
6.45	1	0.2%
6.4958	2	0.4%
6.75	1	0.2%
6.95	1	0.2%
6.975	1	0.2%

Value	Count	Frequency (%)
0	9	2.0%
5	1	0.2%
6.2375	1	0.2%
6.4375	1	0.2%
6.45	1	0.2%
6.75	1	0.2%
6.8583	1	0.2%
6.975	2	0.4%
7.05	2	0.4%
7.125	3	0.7%

Value	Count	Frequency (%)
0	9	2.0%
5	1	0.2%
6.2375	1	0.2%
6.4375	1	0.2%
6.45	1	0.2%
6.75	1	0.2%
6.8583	1	0.2%
6.975	2	0.4%
7.05	2	0.4%
7.125	3	0.7%

Value	Count	Frequency (%)
0	10	2.2%
4.0125	1	0.2%
5	1	0.2%
6.2375	1	0.2%
6.4375	1	0.2%
6.45	1	0.2%
6.4958	2	0.4%
6.75	1	0.2%
6.95	1	0.2%
6.975	1	0.2%

Cabin
['Text', 'Text']

	Dataset A	Dataset B
Distinct	77	76
Distinct (%)	80.2%	82.6%
Missing	350	354
Missing (%)	78.5%	79.4%
Memory size	7.0 KiB	7.0 KiB

Length

	Dataset A	Dataset B
Max length	11	11
Median length	3	3
Mean length	3.65625	3.6630435
Min length	1	1

Characters and Unicode

	Dataset A	Dataset B
Total characters	351	337
Distinct characters	19	18
Distinct categories	1	1 ?
Distinct scripts	1	1 ?
Distinct blocks	1	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

	Dataset A	Dataset B
Unique	60	64 ?
Unique (%)	62.5%	69.6%

Sample

	Dataset A	Dataset B
1st row	C7	D20
2nd row	F33	F33
3rd row	C126	B73
4th row	B49	A7
5th row	C78	B5

Value	Count	Frequency (%)
b96	3	2.7%
b98	3	2.7%
c23	3	2.7%
c25	3	2.7%
c27	3	2.7%
b49	2	1.8%
c78	2	1.8%
c126	2	1.8%
c22	2	1.8%
c26	2	1.8%
Other values (76)	88	77.9%

Value	Count	Frequency (%)
c23	4	3.6%
c25	4	3.6%
c27	4	3.6%
f33	3	2.7%
g6	3	2.7%
b35	2	1.8%
c83	2	1.8%
b58	2	1.8%
b60	2	1.8%
c52	2	1.8%
Other values (75)	82	74.5%

Most occurring characters

Value	Count	Frequency (%)
C	40	11.4%
3	37	10.5%
2	32	9.1%
B	29	8.3%
1	26	7.4%
6	24	6.8%
8	21	6.0%
9	20	5.7%
	17	4.8%
0	16	4.6%
Other values (9)	89	25.4%

Value	Count	Frequency (%)
2	36	10.7%
C	35	10.4%
B	33	9.8%
3	31	9.2%
5	26	7.7%
1	21	6.2%
6	20	5.9%
7	18	5.3%
0	18	5.3%
	18	5.3%
Other values (8)	81	24.0%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	351	100.0%

Value	Count	Frequency (%)
(unknown)	337	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
C	40	11.4%
3	37	10.5%
2	32	9.1%
B	29	8.3%
1	26	7.4%
6	24	6.8%
8	21	6.0%
9	20	5.7%
	17	4.8%
0	16	4.6%
Other values (9)	89	25.4%

Value	Count	Frequency (%)
2	36	10.7%
C	35	10.4%
B	33	9.8%
3	31	9.2%
5	26	7.7%
1	21	6.2%
6	20	5.9%
7	18	5.3%
0	18	5.3%
	18	5.3%
Other values (8)	81	24.0%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	351	100.0%

Value	Count	Frequency (%)
(unknown)	337	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
C	40	11.4%
3	37	10.5%
2	32	9.1%
B	29	8.3%
1	26	7.4%
6	24	6.8%
8	21	6.0%
9	20	5.7%
	17	4.8%
0	16	4.6%
Other values (9)	89	25.4%

Value	Count	Frequency (%)
2	36	10.7%
C	35	10.4%
B	33	9.8%
3	31	9.2%
5	26	7.7%
1	21	6.2%
6	20	5.9%
7	18	5.3%
0	18	5.3%
	18	5.3%
Other values (8)	81	24.0%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	351	100.0%

Value	Count	Frequency (%)
(unknown)	337	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
C	40	11.4%
3	37	10.5%
2	32	9.1%
B	29	8.3%
1	26	7.4%
6	24	6.8%
8	21	6.0%
9	20	5.7%
	17	4.8%
0	16	4.6%
Other values (9)	89	25.4%

Value	Count	Frequency (%)
2	36	10.7%
C	35	10.4%
B	33	9.8%
3	31	9.2%
5	26	7.7%
1	21	6.2%
6	20	5.9%
7	18	5.3%
0	18	5.3%
	18	5.3%
Other values (8)	81	24.0%

Embarked
Categorical

	Dataset A	Dataset B
Distinct	3	3
Distinct (%)	0.7%	0.7%
Missing	1	1
Missing (%)	0.2%	0.2%
Memory size	7.0 KiB	7.0 KiB

S	323
C	82
Q	40

S	330
C	79
Q	36

Length

	Dataset A	Dataset B
Max length	1	1
Median length	1	1
Mean length	1	1
Min length	1	1

Characters and Unicode

	Dataset A	Dataset B
Total characters	445	445
Distinct characters	3	3
Distinct categories	1	1 ?
Distinct scripts	1	1 ?
Distinct blocks	1	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

	Dataset A	Dataset B
Unique	0	0 ?
Unique (%)	0.0%	0.0%

Sample

	Dataset A	Dataset B
1st row	S	S
2nd row	Q	C
3rd row	C	S
4th row	C	S
5th row	Q	C

Common Values

Value	Count	Frequency (%)
S	323	72.4%
C	82	18.4%
Q	40	9.0%
(Missing)	1	0.2%

Value	Count	Frequency (%)
S	330	74.0%
C	79	17.7%
Q	36	8.1%
(Missing)	1	0.2%

Length

Histogram of lengths of the category

Common Values (Plot)

Dataset A

Dataset B

Value	Count	Frequency (%)
s	323	72.6%
c	82	18.4%
q	40	9.0%

Value	Count	Frequency (%)
s	330	74.2%
c	79	17.8%
q	36	8.1%

Most occurring characters

Value	Count	Frequency (%)
S	323	72.6%
C	82	18.4%
Q	40	9.0%

Value	Count	Frequency (%)
S	330	74.2%
C	79	17.8%
Q	36	8.1%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	445	100.0%

Value	Count	Frequency (%)
(unknown)	445	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
S	323	72.6%
C	82	18.4%
Q	40	9.0%

Value	Count	Frequency (%)
S	330	74.2%
C	79	17.8%
Q	36	8.1%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	445	100.0%

Value	Count	Frequency (%)
(unknown)	445	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
S	323	72.6%
C	82	18.4%
Q	40	9.0%

Value	Count	Frequency (%)
S	330	74.2%
C	79	17.8%
Q	36	8.1%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	445	100.0%

Value	Count	Frequency (%)
(unknown)	445	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
S	323	72.6%
C	82	18.4%
Q	40	9.0%

Value	Count	Frequency (%)
S	330	74.2%
C	79	17.8%
Q	36	8.1%

Dataset A

Dataset B

Dataset A

Dataset B

Dataset A

Dataset B

Dataset A

Dataset B

Dataset A

Dataset B

Dataset A

Dataset B

Dataset A

Dataset B

Dataset A

Dataset B

Dataset A

Dataset B

Dataset A

Dataset B

Dataset A

Dataset B

Dataset A

Dataset B

Dataset A

Dataset B

Dataset A

Dataset B

Dataset A

Dataset B

Dataset A

Dataset B

Dataset A

Dataset B

Dataset A

Dataset B

Dataset A

Dataset B

Dataset A

Dataset B

Dataset A

Dataset B

Dataset A

Dataset B

Dataset A

Dataset B

Dataset A

Dataset B

Dataset A

Dataset B

Dataset A

Dataset B

Dataset A

	Age	Embarked	Fare	Parch	PassengerId	Pclass	Sex	SibSp	Survived
Age	1.000	0.071	0.115	-0.262	0.031	0.268	0.080	-0.185	0.102
Embarked	0.071	1.000	0.193	0.038	0.000	0.253	0.054	0.111	0.039
Fare	0.115	0.193	1.000	0.393	-0.035	0.469	0.147	0.473	0.338
Parch	-0.262	0.038	0.393	1.000	0.002	0.000	0.297	0.422	0.154
PassengerId	0.031	0.000	-0.035	0.002	1.000	0.000	0.083	-0.106	0.000
Pclass	0.268	0.253	0.469	0.000	0.000	1.000	0.088	0.137	0.384
Sex	0.080	0.054	0.147	0.297	0.083	0.088	1.000	0.240	0.527
SibSp	-0.185	0.111	0.473	0.422	-0.106	0.137	0.240	1.000	0.203
Survived	0.102	0.039	0.338	0.154	0.000	0.384	0.527	0.203	1.000

Dataset B

	Age	Embarked	Fare	Parch	PassengerId	Pclass	Sex	SibSp	Survived
Age	1.000	0.089	0.055	-0.240	0.018	0.192	0.131	-0.240	0.124
Embarked	0.089	1.000	0.215	0.000	0.000	0.232	0.024	0.024	0.154
Fare	0.055	0.215	1.000	0.431	0.006	0.468	0.216	0.426	0.256
Parch	-0.240	0.000	0.431	1.000	-0.026	0.033	0.273	0.433	0.159
PassengerId	0.018	0.000	0.006	-0.026	1.000	0.000	0.000	-0.073	0.112
Pclass	0.192	0.232	0.468	0.033	0.000	1.000	0.162	0.112	0.391
Sex	0.131	0.024	0.216	0.273	0.000	0.162	1.000	0.183	0.587
SibSp	-0.240	0.024	0.426	0.433	-0.073	0.112	0.183	1.000	0.140
Survived	0.124	0.154	0.256	0.159	0.112	0.391	0.587	0.140	1.000

Dataset A

A simple visualization of nullity by column.

Dataset B

A simple visualization of nullity by column.

Dataset A

Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Dataset B

Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Dataset A

The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Dataset B

The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Dataset A

	PassengerId	Survived	Pclass	Name	Sex	Age	SibSp	Parch	Ticket	Fare	Cabin	Embarked
623	624	0	3	Hansen, Mr. Henry Damsgaard	male	21.0	0	0	350029	7.8542	NaN	S
885	886	0	3	Rice, Mrs. William (Margaret Norton)	female	39.0	0	5	382652	29.1250	NaN	Q
378	379	0	3	Betros, Mr. Tannous	male	20.0	0	0	2648	4.0125	NaN	C
852	853	0	3	Boulos, Miss. Nourelain	female	9.0	1	1	2678	15.2458	NaN	C
126	127	0	3	McMahon, Mr. Martin	male	NaN	0	0	370372	7.7500	NaN	Q
318	319	1	1	Wick, Miss. Mary Natalie	female	31.0	0	2	36928	164.8667	C7	S
732	733	0	2	Knight, Mr. Robert J	male	NaN	0	0	239855	0.0000	NaN	S
516	517	1	2	Lemore, Mrs. (Amelia Milley)	female	34.0	0	0	C.A. 34260	10.5000	F33	S
234	235	0	2	Leyson, Mr. Robert William Norman	male	24.0	0	0	C.A. 29566	10.5000	NaN	S
85	86	1	3	Backstrom, Mrs. Karl Alfred (Maria Mathilda Gustafsson)	female	33.0	3	0	3101278	15.8500	NaN	S

Dataset B

	PassengerId	Survived	Pclass	Name	Sex	Age	SibSp	Parch	Ticket	Fare	Cabin	Embarked
172	173	1	3	Johnson, Miss. Eleanor Ileen	female	1.00	1	1	347742	11.1333	NaN	S
26	27	0	3	Emir, Mr. Farred Chehab	male	NaN	0	0	2631	7.2250	NaN	C
755	756	1	2	Hamalainen, Master. Viljo	male	0.67	1	1	250649	14.5000	NaN	S
706	707	1	2	Kelly, Mrs. Florence "Fannie"	female	45.00	0	0	223596	13.5000	NaN	S
591	592	1	1	Stephenson, Mrs. Walter Bertram (Martha Eustis)	female	52.00	1	0	36947	78.2667	D20	C
797	798	1	3	Osman, Mrs. Mara	female	31.00	0	0	349244	8.6833	NaN	S
147	148	0	3	Ford, Miss. Robina Maggie "Ruby"	female	9.00	2	2	W./C. 6608	34.3750	NaN	S
423	424	0	3	Danbom, Mrs. Ernst Gilbert (Anna Sigrid Maria Brogren)	female	28.00	1	1	347080	14.4000	NaN	S
517	518	0	3	Ryan, Mr. Patrick	male	NaN	0	0	371110	24.1500	NaN	Q
791	792	0	2	Gaskell, Mr. Alfred	male	16.00	0	0	239865	26.0000	NaN	S

Dataset A

	PassengerId	Survived	Pclass	Name	Sex	Age	SibSp	Parch	Ticket	Fare	Cabin	Embarked
564	565	0	3	Meanwell, Miss. (Marion Ogden)	female	NaN	0	0	SOTON/O.Q. 392087	8.0500	NaN	S
360	361	0	3	Skoog, Mr. Wilhelm	male	40.0	1	4	347088	27.9000	NaN	S
620	621	0	3	Yasbeck, Mr. Antoni	male	27.0	1	0	2659	14.4542	NaN	C
113	114	0	3	Jussila, Miss. Katriina	female	20.0	1	0	4136	9.8250	NaN	S
354	355	0	3	Yousif, Mr. Wazli	male	NaN	0	0	2647	7.2250	NaN	C
269	270	1	1	Bissette, Miss. Amelia	female	35.0	0	0	PC 17760	135.6333	C99	S
347	348	1	3	Davison, Mrs. Thomas Henry (Mary E Finck)	female	NaN	1	0	386525	16.1000	NaN	S
565	566	0	3	Davies, Mr. Alfred J	male	24.0	2	0	A/4 48871	24.1500	NaN	S
349	350	0	3	Dimic, Mr. Jovan	male	42.0	0	0	315088	8.6625	NaN	S
571	572	1	1	Appleton, Mrs. Edward Dale (Charlotte Lamson)	female	53.0	2	0	11769	51.4792	C101	S

Dataset B

	PassengerId	Survived	Pclass	Name	Sex	Age	SibSp	Parch	Ticket	Fare	Cabin	Embarked
676	677	0	3	Sawyer, Mr. Frederick Charles	male	24.5	0	0	342826	8.0500	NaN	S
700	701	1	1	Astor, Mrs. John Jacob (Madeleine Talmadge Force)	female	18.0	1	0	PC 17757	227.5250	C62 C64	C
237	238	1	2	Collyer, Miss. Marjorie "Lottie"	female	8.0	0	2	C.A. 31921	26.2500	NaN	S
856	857	1	1	Wick, Mrs. George Dennick (Mary Hitchcock)	female	45.0	1	1	36928	164.8667	NaN	S
39	40	1	3	Nicola-Yarred, Miss. Jamila	female	14.0	1	0	2651	11.2417	NaN	C
546	547	1	2	Beane, Mrs. Edward (Ethel Clarke)	female	19.0	1	0	2908	26.0000	NaN	S
632	633	1	1	Stahelin-Maeglin, Dr. Max	male	32.0	0	0	13214	30.5000	B50	C
472	473	1	2	West, Mrs. Edwy Arthur (Ada Mary Worth)	female	33.0	1	2	C.A. 34651	27.7500	NaN	S
624	625	0	3	Bowen, Mr. David John "Dai"	male	21.0	0	0	54636	16.1000	NaN	S
219	220	0	2	Harris, Mr. Walter	male	30.0	0	0	W/C 14208	10.5000	NaN	S

Dataset A

	PassengerId	Survived	Pclass	Name	Sex	Age	SibSp	Parch	Ticket	Fare	Cabin	Embarked	# duplicates
Dataset does not contain duplicate rows.

Dataset B

	PassengerId	Survived	Pclass	Name	Sex	Age	SibSp	Parch	Ticket	Fare	Cabin	Embarked	# duplicates
Dataset does not contain duplicate rows.

Overview

Variables

Common Values

Length

Common Values (Plot)

Dataset A

Dataset B

Most occurring characters

Most occurring categories

Most frequent character per category

(unknown)

Most occurring scripts

Most frequent character per script

(unknown)

Most occurring blocks

Most frequent character per block

(unknown)

Common Values

Length

Common Values (Plot)

Dataset A

Dataset B

Most occurring characters

Most occurring categories

Most frequent character per category

(unknown)

Most occurring scripts

Most frequent character per script

(unknown)

Most occurring blocks

Most frequent character per block

(unknown)

Most occurring characters

Most occurring categories

Most frequent character per category

(unknown)

Most occurring scripts

Most frequent character per script

(unknown)

Most occurring blocks

Most frequent character per block

(unknown)

Common Values

Length

Common Values (Plot)

Dataset A

Dataset B

Most occurring characters

Most occurring categories

Most frequent character per category

(unknown)

Most occurring scripts

Most frequent character per script

(unknown)

Most occurring blocks

Most frequent character per block

(unknown)

Most occurring characters

Most occurring categories

Most frequent character per category

(unknown)

Most occurring scripts

Most frequent character per script

(unknown)

Most occurring blocks

Most frequent character per block

(unknown)

Most occurring characters

Most occurring categories

Most frequent character per category

(unknown)

Most occurring scripts

Most frequent character per script

(unknown)

Most occurring blocks

Most frequent character per block

(unknown)

Common Values

Length

Common Values (Plot)